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The cost of bad data: stats

‘Bad data’ perhaps sounds a little melodramatic to some, an example of anthropomorphism. But the fact is that customer data is indeed about people, and if that data is incorrect, your business cannot even hope for success.

‘Data is the new oil’, as we keeping hearing, and in a phrase I am taking complete credit for here, ‘oil needs to cleaned before it becomes petrol or kerosene’. (Strictly speaking, oil is fractionally distilled, but you get the point).

New research from Experian Data Quality shows that inaccurate data has a direct impact on the bottom line of 88% of companies, with the average company losing 12% of its revenue.

This loss of revenue comes from wasted marketing spend, wasted resources, and wasted staff time. In this post I'll highlight some more surprising stats from the study.

The context

The hidden cost of bad data may be even greater than that 12% lost revenue. 28% of those who have had problems delivering email say that customer service has suffered as a result, while 21% experienced reputational damage.

The question here is not just one of increasing conversion at source, but one of protecting a company from the risk that comes from sending communications with incorrect contact information. In the US, this risk is high as ISPs increasingly crack down on email senders.

All of this forms the background to a key trend in marketing. Customer data (and in turn, CRM systems, marketing automation, customer experience management and analytics packages) remains a priority for improvement.

The EDQ survey was taken by more than 1,200 organisations in the UK, US and Europe, across a range of sectors and company sizes. Questions regarded how companies collect their contact data, how they manage it, and what impact bad data has on their business.

Email bounce back and loyalty lapse

Similarly, a high proportion of loyalty programmes have suffered through bad contact data. More than 70% of organisations running these programmes reported problems, with inaccurate customer information (34%) the chief cause.

Comments (2)

Thanks for highlighting this very crucial issue, Ben. I always stress to clients how incredibly valuable their database is and how important it is to clean it and nurture it.

The biggest problem we find is the time and place when the data is captured and can fully agree with the as the report on inaccuracies. However, we also find that people tell lies ..... they give false addresses and this can skew the data significantly too.

Interesting stats. Do you think businesses take ownership of the quality level of their data, or blame the customer?

Postcode Anywhere blogged earlier this year about the culture of companies blaming the customer for bad data, rather than looking at their own system / process. I think we should look at the fundamentals of what we are trying to collect and why, and build forms that help get the right data (not just everything with we want in one go!). The classic case is asking for a phone number when someone signs up for email newsletters. By not seeming to be relevant, there is a danger of collecting a lot of bogus numbers (how many times have you seen 0123456789 in a telephone field?!).

Validation at the point of entry goes a long way to prevent dirty data, especially when information is relayed over the phone to a contact centre adviser. If over half the respondents of this survey collect data from a contact centre, then it goes to show how relevant that touchpoint is and the importance of verifying data 'as-it-happens'.

The less time a customer spends on the phone line the better for everyone. The customer leaves happy, the advisor is confident in the data, and the waiting time is reduced to the next in line.

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